print.cv.glmgraph {glmgraph} | R Documentation |
Print a summary of the cv.glmgraph solution path information during cross validation
## S3 method for class 'cv.glmgraph' print(x, ...)
x |
fitted |
... |
Other parameters to |
The call prints the cvmat
object from a fitted cv.glmgraph
object.
The call also prints the chosen regularization parameters lambda1 and lambda2 along with best cv.type
(minimum "mse" or "mae" if family
is "gaussian"; maximum "auc" or minimum "deviance" if family
is "binomial") after cross validation.
Li Chen <li.chen@emory.edu> , Jun Chen <chen.jun2@mayo.edu>
Li Chen. Han Liu. Hongzhe Li. Jun Chen. (2015) glmgraph: Graph-constrained Regularization for Sparse Generalized Linear Models.(Working paper)
cv.glmgraph
set.seed(1234) library(glmgraph) n <- 100 p1 <- 10 p2 <- 90 p <- p1+p2 X <- matrix(rnorm(n*p), n,p) magnitude <- 1 A <- matrix(rep(0,p*p),p,p) A[1:p1,1:p1] <- 1 A[(p1+1):p,(p1+1):p] <- 1 diag(A) <- 0 btrue <- c(rep(magnitude,p1),rep(0,p2)) intercept <- 0 eta <- intercept+X%*%btrue ### construct laplacian matrix from adjacency matrix diagL <- apply(A,1,sum) L <- -A diag(L) <- diagL ### gaussian Y <- eta+rnorm(n) cv.obj <- cv.glmgraph(X,Y,L) print(cv.obj)